Predictors of Early Neurological Deterioration in Stroke Due to Vertebrobasilar Occlusion
Bibliographic record
Abstract
Background and Aims: This study explores the predictors of early neurological deterioration (END) in patients with vertebrobasilar occlusion (VBO) in both primary endovascular therapy (EVT) and medical management (MM) groups. Methods: Patients diagnosed with VBO from 2010 to 2018 were included. Comparative and multivariate analyses were used to identify predictors of all-cause END in the EVT group, and END due to ischemia progression (END-IP) in the MM group. Results: In 174 patients with VBO, 43 had END. In the primary EVT group (N = 66), 17 all-cause END occurred. Distal basilar occlusion (odds ratio (OR), 14.5 [95% confidence interval (CI), 1.4–154.4]) and reperfusion failure (eTICI < 2b67 (OR, 5.0 [95% CI, 1.3–19.9]) were predictive of END in multivariable analysis. In the MM group (N=108), 17 END-IP occurred. Higher systolic blood pressure (SBP) at presentation (per 10 mmHg increase, OR, 1.5 [95% CI, 1.1–2.0]), stroke onset-to-door time <24 h (OR, 5.3 [95% CI, 1.1–2.0]), near-total occlusions (OR, 4.9 [95% CI, 1.2–19.6]), lower posterior circulation-Alberta Stroke Program Early CT scores (OR, 1.6 [95% CI, 1.0–2.5]), and lower BATMAN collateral scores (OR, 1.6 [95% CI, 1.1–2.2]) were predictive of END-IP. Conclusions: In patients with stroke due to VBO, potential predictors of END can be identified. In the primary EVT group, failure to achieve reperfusion and distal basilar occlusion were associated with all-cause END. In the MM group, higher SBP at presentation, onset-to-door time less than 24 h, incomplete occlusions, larger infarct cores, and poorer collaterals were associated with END-IP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".